Evidence map›Paper›PMID 40207007›Full record

ReviewMayo Clinic proceedings. Digital health2025

Global Harmonization of Artificial Intelligence-Enabled Software as a Medical Device Regulation: Addressing Challenges and Unifying Standards.

Sandeep Reddy

Abstract readReview
In one paragraph

Review in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. QHealthcare (Basel, Switzerland) · 2026
    Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Review
  19. FDA-Cleared Artificial Intelligence Medical Devices in Orthopaedic Surgery.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Sandeep ReddyHealth Management and Leadership Discipline, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Queensland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing incorporation of artificial intelligence (AI) into medical device software offers substantial prospects and regulatory hurdles. As AI software as a medical device (AI-SaMD) continues to advance, ensuring its safety, effectiveness, and security is paramount. Nevertheless, the regulatory environment needs more cohesion, with various regions implementing diverse strategies. This paper underscores the necessity for globally harmonized AI-SaMD regulations by examining key regulatory frameworks from the United States, the European Union, China, and Australia. The article also explores crucial elements for harmonization, including algorithm transparency, risk management, data security, and clinical evaluation. Furthermore, the paper advocates for implementing international standards and global data security protocols, emphasizing the significance of cross-border cooperation to ensure the worldwide safety and efficacy of AI-SaMD.

Identifiers

PMID40207007
PMCPMC11975980

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.